Is Deer Movement affected by Recreational Activity?

Submission for the MSc Module Computational Movement Analysis

1 Introduction

Outdoor recreation has gained increasing importance over the last decades, with both the amount of activites increasing and the activity types becoming more active (Bürgi, Lamprecht, and Stamm 2021). Forests are consequently under pressure to cater for this increased demand in active recreation without impairing other ecosystem functions and services. One point of contention and occasional conflict is the disturbance of wildlife by human recreational activities (Wilkes-Allemann, Pütz, and Hirschi 2015). Free-living animals often react to human presence similarly to the presence of natural predators, ranging from physiological stress responses, behavioural changes or reduction in reproductive success (Beale and Monaghan 2004). Responses can be short-term, such as fleeing or even lead to long-term responses, such as avoidance. Due to advances in data collection technologies, such as GPS collars, accelerometers and even heart rate monitors more information regarding animal movement and behaviour can be collected to better understand the influence of human recreation on movement patterns. Determining the movement and activity of humans has also advanced, from traditional counters to precise GPS tracks from outdoor activity apps.

In this study GPS recordings of roe deer Capreolus capreolus from a popular forest in the vicinity of Zürich Switzerland was analysed for changes in spatio-temporal activity in response to human recreational activities. To provide a proxy for recreational activity data from activity app Strava was combined with popular hiking routes. Based on a brief overview of the literature a set of hypotheses were established and explored (Table 1).

Table 1: The examined null and alternate hypotheses to explore the influence of human recreation on the spatio-temporal behaviour of roe deer.

Null hypothesis	Alternate hypothesis
Deer movement does not vary between weekdays and weekend	Deer movement on average changes during the weekend
Deer movement is not constrained by recreational paths	Deer movement is constrained by recreational paths
There is no relationship between the popularity of recreational paths and deer activitiy	Deer movement is influenced by the popularity of recreational paths

2 Material and Methods

2.1 Datasets

The deer dataset was provided as a result of a research project conducted in partnership with the Wildnispark Sihlwald. The Wildnispark Zurich consists of a protected core area and a surrounding area with high recreational use due to its proximity to the city of Zurich. The movement of 14 deer was tracked using GPS trackers and the contained attributes were summarisedin {#tbl-structure}.

Table 2: Data structure

Name	Purpose
Latitude	Positioning data
Longitude	Positioning data
x	Positioning data (projected)
y	Positioning data (projected)
reh	ID of individual deer
datetime_utc	Date and Timestamp

The characteristics of the data limit the computational movement analysis methods that can be applied.

Col1	Col2	Col3	Col4

To examine recreational activities additional data from the fitness tracking platform Strava was gathered by scraping the publicly available global heatmap using a script developed by Nils Ratnaweera. The extracted raster tiles contained colour values to indicate the number of activities. This provided a relative indication of how often a route was frequented. All activity types were (cycling, running etc.) were included and the dataset contained no temporal data. Lastly street data in the publicly available swissTLM3d dataset provided by swisstopo was used to examine the influence of recreational paths.

2.2 Preprocessing and Exploratory Analysis

The deer data was re-projected to LV95 and the Euclidean distance and time lag were calculated to perform further movement analysis. Based on the spatial extent of the entire dataset a bounding polygon was generated and used to crop Strava activity data and recreational path data.

The deer dataset consists of 14 individual deer, tracked over differing periods between 2013 and 2016 (Figure 1).

Figure 1: Numer of deer and respective sampling periods.

Multiple sampling regimes, in intervals of 180 and 5 minutes were used (Figure 2). The shorter sampling periods were applied inconsistently and had a short average duration of 6 hours. The longer intervals of 180 minutes was therefore preferred to examine the long-term relationships between deer movement and recreational activity. Temporal outliers were removed by filtering to ± 2 minutes of the long sampling interval.

(a) Timelag distribution

(b) Distance distribution

Figure 2: Frequency plots

Several spatial outliers were identified in Figure 3 and roughly removed by filtering the data to a step length of less than 2500 meters.

Figure 3: Spatial extent of the deer dataset, outliers in red.

Due to the large number of individual deer temporal extent a subset of deer (RE08, RE11, RE06) was chosen due to their relatively high concurrent activity levels (average distance moved between measurements), spatial proximity to recreational infrastructure and spatial independence from one another. The multi-annual sampling period was also reduced to examining 2014, as there was continuous and well-sampled data for the chosen deer groups (Figure 4). This was further reduced to examining the months of January, April, July and October.

(a) Spatial distribution of selected deer

(b) Sampling frequency of selected deer

Figure 4: Deer subset chosen for further analysis

2.3 Methods

2.3.1 Differences in movement between the weekdays and the weekend

Filtered movement data was grouped by deer, month and whether movement was captured on a weekday or on a weekend. Average step length was calculated for each grouping and values for each month and weekday:weekend were compared using an ANOVA. A log transformation was applied to achieve a more normal distribution of the model residuals.

2.3.2 Impact of recreational paths

Two methods were used to examine the influence of recreational paths on deer movement. The first method used examined the number of times a deer trajectory crossed recreational paths and if there was any variation between weekdays and weekends.

(a) Deer 06

(b) Deer 08

(c) Deer 11

Figure 5: Deer movement in relation to hiking paths

The second method involved creating multiple buffers in 5m increments (5m, 10m, 15m, 20m, 25m) around recreational paths. For each examined deer and month the spatial extent and number of recorded points was used to generate a random distribution of points. Using an intersect the frequency of points within different buffers was compared to determine whether the relationship between deer and recreational paths follows a non-random pattern.

(a) Deer 06

(b) Deer 08

(c) Deer 11

Figure 6: Comparing simulated points to deer distribution

2.3.3 Role of path popularity

The influence of path popularity was determined by extracting raster values from the Strava heatmap and assigning them to the individual segments from the street data. A single 25m buffer around all streets was used to determine the number of deer recorded in the vicinity and a Spearman Rank correlation was used to determine whether path popularity and deer presence correlated.

(a) All streets

(b) Hiking paths

Figure 7: Popularity data from Strava extracted to path data

3 Results and Discussion

3.1 Differences in movement between the weekdays and the weekend

The ANOVA results (Table 3) indicated that no significant difference in mean step length between weekdays and weekends, meaning the null hypothesis is accepted (Table 1). Whilst significant differences were observed between sample months and individual deer no significant interaction with whether data was recorded on the weekend was found.

Table 3: ANOVA Results

Term	Degrees of freedom	Sum of squares	F-Value	P-Value
weekend	1	0.000	0.000	0.988
month	3	77.302	21.124	0.000
reh	2	49.837	20.428	0.000
weekend:month	3	2.855	0.780	0.505
weekend:reh	2	2.432	0.997	0.369
month:reh	6	32.118	4.388	0.000
weekend:month:reh	6	4.878	0.666	0.677
Residuals	2799	3414.276	NA	NA

Figure 8: Average step length between weekdays and weekend data

A further exploration using hourly averages for the same dataset showed a distinct pattern, matching the natural crepuscular movement of deer (Figure 9). Whilst data granularity was low seasonal shifts associated with shorter days in January were also observed. This implies that aggregated movement data is suitable for examining deer behaviour. However the influence of humans cannot be clearly identified at this scale. This could be a consequence of deer “freezing” in response to perturbations (Marantz et al. 2016), the duration of which does not lead to substantial differences in step length across 3 hour sampling intervals. To better identify the influence of recreational activity a higher temporal granularity, accelerometer data, coupled with richer human recreational data (timestamped activities, counters).

Figure 9: Average step length within a days

3.2 Impact of recreational paths

3.2.1 Comparing crossings on the weekend

When examining the number of crossings across the same subset of deer a clear difference in frequency between weekdays and weekends was found. This indicates that deer are less likely to cross recreational paths during the weekend. This pattern is more in line with observed deer behaviour from literature, such as freezing and avoidance (Coppes et al. 2017). Therefore intersection frequency is a preferable method when deer data has low temporal granularity and aggregated recreational activity is examined.

Figure 10: Number of path crossings, comparing weekend to weekdays

3.2.2 Vicinity to paths compared to randomly generated points

On average, fewer observations were recorded in the vicinity of paths when compared to randomly generated data (Figure 11). This indicates that in addition to reduced crossing of paths over the weekend deer avoided paths on average.

Figure 11: Comparison of deer distribution near paths to random points

When split by deer and month the avoidance behaviour was not consistent, with differences both between deer and between months for the same deer (Figure 12). This indicates that additional factors influence deer behaviour, likely linked to the use of the path. These additional factors could be elucidated by picking specific time windows, linked to perturbation events (i.e. no activity, logging, high activity). Nonetheless comparing real deer distribution to random deer distribution is a promising high level method to examine the effect of certain objects on the spatial distribution of the deer.

Figure 12: Comparison of deer distribution near paths to random points, subset by month and deer

Whilst no statistical analysis of the results was performed the results indicate that built infrastructure has a substantial effect on the spatial distribution of deer in this study.

3.3 Role of path popularity

A visual investigation of RE06 (Figure 13) was used to identify whether path popularity could be established as an aforementioned additional influencing factor. The distribution of points was clearly affected by the more popular path. Whilst deer measurements were largely concentrated between two paths, the deer was more likely to cross the less frequented secondary paths.

(a) Distribution of point data

(b) Kernel density estimation of point data (<0.8 cutoff)

Figure 13: Distribution of RE06 over sample period

However further investigation involving the previously utilised subset of deer indicated no correlation between path popularity and deer presence (Figure 14). Considering that interactions between deer and paths were previously quantified and observed it is likely that the lack of absolute popularity values, temporal context and differentiation between activity types makes it challenging to explore the relationship between the two variables. Platforms that could provide this additional information could there be a valuable and inexpensive source of insights to better understand if a relationship exists or not. This data should also be integrated alongside traditional infra-red counters for ground truthing.

Figure 14: Correlation between popularity and frequency of recorded deer within a 50m buffer of streets

4 Conclusion

As outdoor recreational activities increase in popularity there is a continued need to better understand what the effects are on nearby wildlife. Derived movement data from three deer over the course of 2014 did not show differences in aggregated movement between the weekdays and weekends. However when recreational paths were examined, they were found to influence deer movement, resulting in greater avoidance overall and reduced crossing of the paths during the weekends. To determine responses to individual perturbation events a higher sampling rate, combined with accelerometer data is likely required. The observed avoidance also did not correlate with the popularity of the paths that deer interacted with during the study. Nonetheless, increased outdoor recreation and tracking also presents an opportunity to integrate more activity data. If this data is temporally concurrent with deer tracking, enriched with activity type and verified by stationary counters it is likely to be an invaluable tool to examine long-term interactions between deer and human recreation.

References

Beale, Colin M., and Pat Monaghan. 2004. “Human Disturbance: People as Predation-Free Predators?” Journal of Applied Ecology 41 (2): 335–43. https://doi.org/10.1111/j.0021-8901.2004.00900.x.

Bürgi, Rahel, Markus Lamprecht, and Hanspeter Stamm. 2021. “Sport Schweiz 2020.” Magglingen.

Coppes, Joy, Friedrich Burghardt, Robert Hagen, Rudi Suchant, and Veronika Braunisch. 2017. “Human Recreation Affects Spatio-Temporal Habitat Use Patterns in Red Deer (Cervus Elaphus).” PLOS ONE 12 (5): e0175134. https://doi.org/10.1371/journal.pone.0175134.

Marantz, Sierra A., Jed A. Long, Stephen L. Webb, Kenneth L. Gee, Andrew R. Little, and Stephen Demarais. 2016. “Impacts of Human Hunting on Spatial Behavior of White-Tailed Deer (Odocoileus Virginianus).” Canadian Journal of Zoology 94 (12): 853–61. https://doi.org/10.1139/cjz-2016-0125.

Wilkes-Allemann, Jerylee, Marco Pütz, and Christian Hirschi. 2015. “Governance of Forest Recreation in Urban Areas: Analysing the Role of Stakeholders and Institutions Using the Institutional Analysis and Development Framework.” Environmental Policy and Governance 25 (2): 139–56. https://doi.org/10.1002/eet.1668.